Plant Gene and Trait 2024, Vol.15, No.1, 1-7 http://genbreedpublisher.com/index.php/pgt 5 Machine learning models have also played a role in exploring and explaining the mechanism of wheat drought response. Through the analysis and pattern recognition of a large number of data, these models are helpful to understand the ways and key factors of wheat response to drought, and provide new ideas and methods for further study of wheat drought resistance mechanism. 4 Challenges and Opportunities 4.1 Data quality and availability The success of machine learning algorithms depends heavily on the quality and availability of data. In predicting wheat response to drought, data quality directly affects the accuracy and reliability of the algorithm, and high-quality data can provide the diversity and representativeness required by the model, but in the field of agriculture, data quality often faces multiple challenges (Ambarwari et al., 2020). Data quality problems can result from errors in the data collection process, including but not limited to missing data, outliers, labeling errors, or inaccurate labeling. To solve these problems, data cleaning, standardization and correction are needed to ensure the integrity and accuracy of data. Data availability is also a challenge. Agricultural data often comes from multiple sources, and the format and standards are inconsistent, so it needs to be integrated and unified. In addition, some data may not be publicly available or shared, making data acquisition difficult. To solve the problem of data quality and availability, data preprocessing technology, feature engineering and data integration methods should be integrated. At the same time, it is necessary to strengthen the standardization of data collection and data sharing in order to make more extensive use of high-quality data for the training and optimization of machine learning models. Effective handling of data quality and availability issues will help improve the accuracy and practicality of machine learning algorithms in predicting wheat drought response. 4.2 Model generalization ability The model generalization ability of a machine learning algorithm is a key factor in evaluating its performance on new data, which refers to the model's performance on previously unseen data. For predicting wheat response to drought, the generalization ability of the model determines its applicability and reliability in real scenarios. If a model performs well only on training data, but poorly on new data, it indicates that the model is overfitting. Overfitting means that the model overadapts to the characteristics of training data, resulting in poor generalization ability on new data. On the contrary, if the model performs well on both training data and new data, it indicates that the model has strong generalization ability (Cao et al., 2021). For the prediction of wheat response to drought, the model generalization ability is affected by data quality, model complexity and training methods. In order to improve the model generalization ability, appropriate model evaluation methods, such as cross-validation and data set partitioning, should be adopted. In addition, techniques such as feature selection and model regularization also help reduce overfitting and improve the generalization ability of the model. When machine learning algorithm is applied to wheat drought response prediction, evaluating the generalization ability of the model is an important step to ensure the reliability and practicability of the model. A model with good generalization ability can predict wheat response to drought more accurately and provide more accurate guidance and decision support for agricultural production. 4.3 Comparison with the effect of traditional research methods The advantages and limitations of the machine learning algorithm and the traditional research methods were compared for wheat drought response. Traditional research methods focus on laboratory observation, physiological testing and statistical analysis when exploring wheat drought response. These methods are conducive to in-depth understanding of physiological processes, but limited by the size and complexity of data, it is difficult to fully capture the comprehensive impact of drought on wheat growth and yield. Relatively speaking, machine learning algorithms rely on large data sets and algorithm learning to perform well in processing
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